Systems Engineering Set-Based Design (SBD) and Optimization Can - PowerPoint PPT Presentation

Systems Engineering Set-Based Design (SBD) and Optimization … Can they Live Together and Make Program Trade Space Decisions Better? Author(s): Dr. Stephen Rapp, Senior Research Scientist, US Army TARDEC <Presenter> Dr. Ratna Babu Chinnam, Professor Systems Engineering, Wayne State University (WSU) Dr. Norbert Doerry, Technical Director, Naval Sea Systems Command Technology Office Dr. Leslie Monplaisir, Chairman/Professor Systems Engineering, WSU Dr. Alper Murat, Associate Professor, WSU Dr. Gary Witus, Associate Research Professor, WSU 8/14/2018 DISTRIBUTION STATEMENT A. Approved for public release; distribution unlimited. 1

Systems Outline Engineering • “Three Minute Thesis” • Importance & Relevance • Research Problem – Scope – Goals and Objectives • Research Methodology – PD Set Solution Logic – Quantitative Framework • Analysis & Results • Contributions • Conclusion Core funding: DoD STEM ASEE Doctoral Fellowship through the SMART Scholarship Program Command Sponsorship: U.S. Army RDECOM TARDEC Additional Support: NAVSEASYSCOM and MARCORSYSCOM Partial funding: Systems Engineering Research Center, OSD OUSD R&E (US), Contract Number - HQ0034-13-D-0004 DISTRIBUTION STATEMENT A. Approved for public release; distribution unlimited. 2

Systems “Three Minute Thesis” Engineering Starting out … “I’m • Problem : How do we solve a going to build the best sports car ever trajectory of sequential decisions …. My Shangri - La!” Where’s a in Product Development (PD)? sports car in – Every decision is not optimal Shangri-La? for a point in time – Data is uncertain and fog around it only yields over time, while you are designing and developing • Insight : Decisions need optimal actions for design trajectory and Am I even not immediately for design itself on the right mountain? • Solution : We created a Quantitative Framework that values the trajectory actions resiliently through time for PD DISTRIBUTION STATEMENT A. Approved for public release; distribution unlimited. 3

Systems Importance & Relevance Engineering Philosophical to Qualitative • Pascal’s Wager in 1664 …. “ Heaven vs. Fun and …. maybe Hell” – One cannot judge performance by results, but by alternative costs. – Decision quality can’t be judged solely by its outcome. However, this is typically voiced by the people who fail (Taleb 2009). – Those who succeed tie it to their own smart decisions – Extending this to Product Development (PD) • Is risk of high cost development failure worth product breakthrough? • Toyota … “kaizen” gradual vs. “kaikaku” breakthrough – Set-Based Design (SBD) is an integral part of Toyota Product Development System (Liker 1999) – Toyota is more kaizen, but recognizes the need to deal with kaikaku uncertainty during development – SBD allows a gradual “ necking down” of options during PD DISTRIBUTION STATEMENT A. Approved for public release; distribution unlimited. 4

Systems Importance & Relevance Engineering Adding Quantitative to Qualitative • Defense Programs are more “ kaikaku ” by necessity – Uncertainty impacting programs (over budget/schedule delay) is driving SBD acceptance – Current SBD programs are expanding and there is need for quantification – SBD critical to add design flexibility & resilience • Specific industry efforts documented SBD to deal with design uncertainty • Design “Trade Space” exploration methods tied to SBD are being considered – Focus: expand design trade space • Design “Process Resilience” is well documented, both with and without SBD – Resilience in PD ties to expanding design trade space • Mathematical methods to model uncertainty are developed but less applied to SBD – Current efforts are focused on domain applications or are specific techniques • The need to model risk and uncertainty in PD is significant – Deterministic estimation misses the mark • Set solutions postulated to reduce uncertainty under changing requirements • Data quality and maturity are crucial factors impacting design quality Bottom line: Need a quantitative framework to utilize SBD for PD! DISTRIBUTION STATEMENT A. Approved for public release; distribution unlimited. 5

Systems Research Problem Engineering Research Scope - Framework Quantitative SBD Framework Research and Design the Mathematics for the Framework Solution Structure: Markov Decision Process Solution Approach: Stochastic Dynamic Programming Solution Derivation and Analysis Set Solution Data – Continuous Update/Decisions Anchored to Epochs but Flexible Data Quality and Maturity Core Design Data: out of Performance Burdens Scope - Size, Weight, Power, etc. - Costs Epoch A Epoch B Epoch Γ Epoch Ω Risk/Uncertainty DISTRIBUTION STATEMENT A. Approved for public release; distribution unlimited. 6

Systems Research Problem Engineering Research Scope – Coverage of the Product Lifecycle SBD for Continuous Modernization: • Views “Solution” as a Specific Design and its Set of Potential Future Options • Constraints and evaluation apply to and address not just cost-vs-capability of initial design, but also cost-vs-capabilities of set of potential future options Multiple stakeholders iteratively Set of potential future trajectories as needs change, technologies mature and funding restricting and relaxing constraints constraints change Acceptance Growth Region Region Initial Production Product Production, Deployment, Operation & Sustainment Development (including modernization, adaptation, extension) DISTRIBUTION STATEMENT A. Approved for public release; distribution unlimited. 7

Systems Research Problem Engineering Goals and Objectives • Goals: I don’t care – Extend SBD to improve resilience of PD process how your with respect to changes in requirements and to iPhone works uncertainties just give it to – Develop a conceptual framework to integrate me or else. quantitative analysis with SBD – Combine SBD and analytics into a new hybrid approach supporting resilient design process – Develop a current case or example as a Proof of Concept to validate the framework • Objectives: – Give a rigorous formulation to principle of SBD – Give an analytical approach for set adjustment decisions – Give an analytical formulation for set adjustment decisions DISTRIBUTION STATEMENT A. Approved for public release; distribution unlimited. 8

Systems Research Methodology Engineering PD Set Solution Logic • Single design consists of individual option selections, one/subsystem of system to be developed • Set Solution is a combination of any possible Point Solutions or specific designs for PD • Visual representation of SBD for considering which designs to develop – Colored arcs represent designs not developed in the previous epoch – In most programs, these are possible but unlikely • For example, SS1 in B going to SS2 in Γ requires catching up all work for D2 missed before B DISTRIBUTION STATEMENT A. Approved for public release; distribution unlimited. 9

Systems Research Methodology Engineering Quantitative Framework - Overview • Framework has control dimensions for time (milestones and development phases) • High-level framework uses a stochastic process to define value, termed Contribution-to-Design – Contribution-to-Design is a weighted and normalized combination of metrics: performance, cost (production) and weight (physical) • Is forward looking and calculated for all subsystem technology option choices and then “rolled up” for higher subsystem and set solution • Metrics have targets, termed: Design Readiness Levels • Solution Approach: Recursive (backward) Dynamic Programming – Bellman’s Equation with no discount to determine optimal investment actions at each milestone/epoch • Contribution-to-Design Values modified to a Markov Decision Process Network: – Action space: Whether or not an option should be invested in – Transition matrix: Probabilities associated with possible state to state movements DISTRIBUTION STATEMENT A. Approved for public release; distribution unlimited. 10